{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T01:07:06Z","timestamp":1789434426113,"version":"build-2803163510"},"reference-count":85,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2018,7,16]],"date-time":"2018-07-16T00:00:00Z","timestamp":1531699200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Amyloid is an insoluble fibrous protein and its mis-aggregation can lead to some diseases, such as Alzheimer\u2019s disease and Creutzfeldt\u2013Jakob\u2019s disease. Therefore, the identification of amyloid is essential for the discovery and understanding of disease. We established a novel predictor called RFAmy based on random forest to identify amyloid, and it employed SVMProt 188-D feature extraction method based on protein composition and physicochemical properties and pse-in-one feature extraction method based on amino acid composition, autocorrelation pseudo acid composition, profile-based features and predicted structures features. In the ten-fold cross-validation test, RFAmy\u2019s overall accuracy was 89.19% and F-measure was 0.891. Results were obtained by comparison experiments with other feature, classifiers, and existing methods. This shows the effectiveness of RFAmy in predicting amyloid protein. The RFAmy proposed in this paper can be accessed through the URL http:\/\/server.malab.cn\/RFAmyloid\/.<\/jats:p>","DOI":"10.3390\/ijms19072071","type":"journal-article","created":{"date-parts":[[2018,7,18]],"date-time":"2018-07-18T08:45:42Z","timestamp":1531903542000},"page":"2071","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":63,"title":["RFAmyloid: A Web Server for Predicting Amyloid Proteins"],"prefix":"10.3390","volume":"19","author":[{"given":"Mengting","family":"Niu","sequence":"first","affiliation":[{"name":"School of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanjuan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2965-9920","authenticated-orcid":false,"given":"Chunyu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150040, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"Han","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Harbin University of Commerce, Harbin 150040, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,7,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1698","DOI":"10.1093\/bioinformatics\/btv027","article-title":"WALTZ-DB: A benchmark database of amyloidogenic hexapeptides","volume":"31","author":"Beerten","year":"2015","journal-title":"Bioinformatics"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1080\/13506129.2017.1339686","article-title":"Localized amyloidogenic immunoglobulin light chain-derived amyloidosis in a young boy and an adolescent girl","volume":"24","author":"Ikeda","year":"2017","journal-title":"Amyloid"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Louros, N.N., Iconomidou, V.A., Giannelou, P., and Hamodrakas, S.J. 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